fairret: a Framework for Differentiable Fairness Regularization Terms
Authors: Maarten Buyl, MaryBeth Defrance, Tijl De Bie
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show the behavior of their gradients and their utility in enforcing fairness with minimal loss of predictive power compared to baselines. Our contribution includes a Py Torch implementation of the FAIRRET framework. We visualize the FAIRRETs gradients and evaluate their empirical performance in enforcing fairness notions compared to baselines. |
| Researcher Affiliation | Academia | Maarten Buyl Ghent University maarten.buyl@ugent.be Mary Beth Defrance Ghent University marybeth.defrance@ugent.be Tijl De Bie Ghent University tijl.debie@ugent.be |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. Appendix E provides 'Code Use Examples' which are snippets of PyTorch code, not pseudocode. |
| Open Source Code | Yes | The framework is available as a package at https://github.com/aida-ugent/fairret. |
| Open Datasets | Yes | Experiments were conducted on the Bank (Moro et al., 2014), Credit Card (Yeh & hui Lien, 2009), Law School4, and ACSIncome (Ding et al., 2021) datasets. |
| Dataset Splits | Yes | To find these hyperparameters, we took the 80%/20% train/test split already generated for each seed, and further divided the train set into a smaller train set and a validation set with relative sizes 80% and 20% respectively. |
| Hardware Specification | Yes | All experiments in Sec. 4 were conducted on an internal server equipped with a 12 Core Intel(R) Xeon(R) Gold processor and 256 GB of RAM. |
| Software Dependencies | No | The paper mentions 'Py Torch', 'cvxpy', and 'Adam optimizer' but does not specify their version numbers or other key software dependencies with version information required for reproducibility. |
| Experiment Setup | Yes | The classifier ℎwas a fully connected neural net with hidden layers of sizes [256, 128, 32] followed by a sigmoid... optimized with the Adam optimizer implementation of Py Torch, with a learning rate of 0.001 and a batch size of 4096. The loss was minimized over 100 epochs, with 𝜆= 0 for the first 20 to avoid constraining ℎbefore it learns anything. |